Global Shutdown: Voices from Universities Around the World
Bibliographic record
Abstract
Digital technologies have had a great impact on higher education institutions (HEI) in recent years, but COVID-19 has propelled the integration of technology into the education sector worldwide. This panel discussion will give an account of different university stories from Europe, North America, and Africa. Universities were faced with the task of offering online or blended learning scenarios overnight. What effects did the shutdown have on their country’s educational sector and HEI? How was digitalization perceived after the lockdown? How did the institution deal with transforming their traditional classes? Are there state or federal policies in place that support and provide mechanisms to address technical issues, social inequalities, accessibility issues and training for faculty and staff? What are the biggest challenges in digital learning that need to be overcome? What lessons were learned and how can we learn from each other. Global learning and virtual exchange can offer new opportunities for the global educational community? Can COVID-19 be a blessing in disguise for the educational community? What lies ahead and is there going to be a “new normal” after this crisis has died down? Each panelist will present a short brief about the educational policies in their respective country by highlighting how their HEI tackled the enormous challenges caused by the pandemic since March 2020. We will hear voices from Bonn-Rhein-Sieg, University of Applied Sciences (Germany), Polytechnic Institute of Viseu (Portugal), Conestoga College, Institute of Technology & Advanced Learning (Canada), Middle Tennessee State University (USA), University of Cape Coast (Ghana), and University of Nairobi (Kenya).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.033 | 0.014 |
| Scholarly communication | 0.033 | 0.018 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".